269results about How to "Reduce subjectivity" patented technology

The invention relates to the technical field of data processing, and discloses an intelligent bridge reinforcement decision generation method, device and equipment based on damage identification. The method comprises the following steps: carrying out bridge damage identification and evaluation on a bridge state data set to obtain a bridge damage characteristic spectrum; performing intelligent decision analysis on the bridge damage characteristic spectrum, generating a preliminary reinforcement scheme, and obtaining a candidate reinforcement strategy set; performing parametric modeling and multi-scale simulation on the candidate reinforcement strategy set, and performing coupling calculation evaluation on a reinforcement effect to obtain an optimized reinforcement scheme; virtual construction planning is conducted on the optimized reinforcement scheme, a construction process model is generated through an intelligent scheduling algorithm, and a target construction scheme is obtained; and monitoring and feeding back the target construction scheme in real time, and dynamically optimizing the construction process through an adaptive control algorithm to obtain a target intelligent reinforcement implementation scheme. The intelligent bridge reinforcement decision generation efficiency and accuracy based on damage identification are improved.

The invention provides an intelligent garden environment monitoring data analysis method and system. Sensors are deployed to collect temperature, humidity and other data in real time to form a data set. Analyzing an environment parameter change mode of each region by adopting a time sequence clustering algorithm, and determining characteristics and trends; and carrying out dimension reduction on the data by using a multi-dimensional scale transformation algorithm to a three-dimensional space for visualization, and generating an environment state map. And evaluating the plant fitness in combination with a plant physiological ecology knowledge base, and generating a scoring matrix to guide the preliminary layout scheme. And dynamically adjusting the layout by applying reinforcement learning, selecting an optimal scheme, and predicting a future environmental evolution path based on the optimal scheme and historical data. An automatic control system framework is constructed, a self-adaptive control algorithm is designed, intelligent regulation and control of plant layout are realized, garden management efficiency is improved, and ecological protection and sustainable development are promoted. Therefore, full-chain intelligent management from environment monitoring to plant layout optimization is realized, and the intelligent level of garden environment monitoring and management is remarkably improved.

The invention provides a tunnel surrounding rock deformation monitoring system and method based on visual identification, and relates to the technical field of tunnel surrounding rock deformation monitoring. According to the method, firstly, point cloud data are analyzed to extract crack features, and image data at historical moments are used as input to construct a visual prediction model based on a convolutional neural network, so that the deformation at the current moment is obtained. Then, the deformation amount, the temperature and the strain data at the current moment are combined to generate a deformation index used for reflecting the abnormal deformation degree. By comparing the deformation index with the preset deformation threshold value, the system can judge whether abnormal deformation exists in the monitoring area or not in time. If the deformation index is lower than a threshold value, marking as normal; and if the threshold value is exceeded, marking as abnormity and sending out early warning. The system provides an efficient and real-time tunnel surrounding rock deformation monitoring solution.

The invention relates to the technical field of display screen color adjustment, and discloses a display screen color adjustment method and system based on environment information, and the method comprises the steps: obtaining display content data, environment color data, environment light data and a display screen type of a display screen; calculating color perception data according to the environment color data and the display screen type, and performing data processing on the color perception data to obtain chromaticity feedback data; establishing a relationship between the ambient light data and user preferences through a support vector machine method to obtain user evaluation data; inputting the chromaticity feedback data and the user evaluation data into a color correction model to obtain color adjustment data; classifying the display content data and calculating a type weight index through a squirrel optimization algorithm to obtain a weight index; and generating a color adjustment instruction according to the color adjustment data and the weight index, and performing adaptive adjustment on the display content. The method solves the problems that the color adjustment of the display screen has high requirements on a light source environment and is strong in subjectivity.

The invention relates to the technical field of production task decision making, in particular to a furniture production task automatic matching method and system based on intelligent manufacturing. And extracting personalized demand information corresponding to the production task, converting the personalized demand information into standard design parameters, and generating a customized task. The production task is disassembled into a plurality of sub-tasks, and each sub-task has independent attributes including process requirements, resource requirements and time requirements; and performing one-to-one mapping on the customized tasks and the subtasks to generate an execution plan. Performing resource matching by using a constraint algorithm based on the execution plan and the existing production resource information to obtain a production plan; and obtaining an optimal scheduling plan by using a constraint algorithm according to the production plan. And based on the real-time production data and the burst information, adjusting the optimal scheduling plan through the data analysis model to obtain a final scheduling plan. The method can cope with emergencies or real-time data changes, and manual decisions are reduced.

The invention discloses an automatic welding method and system based on a collaborative robot, and the method comprises the steps: collecting and preprocessing three-dimensional point cloud data of a to-be-welded steel part and the collaborative robot, and collecting thermal physical property data of to-be-welded steel; based on the three-dimensional point cloud data and the thermophysical property data of the steel, a welding simulation model is constructed through the digital twin technology; constructing a welding quality evaluation model based on a deep learning technology, and performing a virtual welding test by using the welding simulation model and the welding quality evaluation model to obtain an optimal welding route and optimal welding parameters; and based on the optimal welding route and the optimal welding parameters, the collaborative robot is started to conduct automatic welding. The invention relates to the technical field of automatic welding, and solves the technical problem that an existing automatic welding technology lacks welding parameter self-adaptive optimization and welding route intelligent planning through combination of a digital twinning technology and a deep learning technology.

The invention relates to the technical field of informatization war, and particularly provides a big data analysis-based combat decision auxiliary system and method, and the system comprises a data collection module, a data preprocessing module, a multi-source intelligence fusion module, a situation prediction depth mining and analysis module, a military strength deployment module, and an auxiliary optimization module. The method comprises the following steps: S101, data acquisition, connection and preprocessing; s102, fusing multi-source intelligence to judge a target; s103, accurately analyzing and predicting the attempt of the enemy; s104, carrying out multi-dimensional association recommendation on the available strength; s105, assisting in optimizing a combat scheme; according to the scheme, how to apply a big data analysis technology is introduced in detail, a set of efficient and intelligent combat scheme generation system is constructed, and scientific and accurate tactical and strategic guidance is provided for military decision makers. According to the system, key technical links such as data acquisition, fusion, analysis, modeling and visualization are integrated so as to cope with a complex and dynamic battlefield environment, and maximization of combat effectiveness is achieved.

The invention discloses an advertisement budget distribution method and system based on machine learning and user behaviors, and the method comprises the following steps: collecting the historical behavior data of a user, and extracting the feature data of the historical behavior data through feature engineering; constructing a user probability prediction model, and training the model by using the feature data; in an advertisement putting process, collecting real-time behavior data of a user, inputting the real-time behavior data into the trained model, and predicting an advertisement clicking probability of the user and an advertisement commodity purchasing probability of the user; a fusion model is constructed, the fusion model calculates the conversion rate of the user according to the advertisement clicking probability of the user and the advertisement commodity purchasing probability of the user, and calculates the channel conversion rate according to the putting channel; distributing a preliminary advertisement budget according to the historical data of the putting channel; and dynamically adjusting the preliminary advertisement budget of the putting channel based on the channel conversion rate. According to the invention, precise distribution of the advertisement budget can be realized, and the overall conversion effect of the advertisement is improved.

The invention belongs to the technical field of data asset management, and discloses an enterprise data asset auditing method and system based on big data, and the method comprises the steps: collecting financial information data, business operation data, management decision data and external industry data of an enterprise; preprocessing the collected financial information data, business operation data, management decision data and external industry data to obtain a financial feature data set, a business operation feature data set, a management decision feature data set and an external industry feature data set; performing feature extraction and weighted fusion on the financial feature data set, the business operation feature data set, the management decision feature data set and the external industry feature data set to obtain a comprehensive feature data set; constructing an audit risk prediction model, inputting the comprehensive feature data set into the audit risk prediction model, and predicting to obtain an audit risk coefficient of the enterprise data assets; and a more comprehensive view angle is provided, and the data asset risk of the enterprise can be evaluated more accurately.

The invention relates to the technical field of computer vision, and discloses a litchi fruit quality detection method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining the variety of a to-be-detected litchi, and determining a specified excitation wavelength corresponding to the variety of the to-be-detected litchi; setting the excitation wavelength of fluorescence spectrum analysis equipment as the specified excitation wavelength; fluorescence spectrum data and multispectral image data of a plurality of different surface areas of the to-be-detected litchis are obtained respectively, the fluorescence spectrum data and the multispectral image data corresponding to the same surface areas are associated, and the sum of the different surface areas covers the whole surface of the to-be-detected litchis; inputting each group of associated fluorescence spectrum data and multispectral image data of the to-be-detected litchis into a pre-trained fruit quality prediction model, and outputting a quality degradation prediction result of the to-be-detected litchis; and rapid, accurate and lossless prediction of the quality condition of the litchi fruits is realized.

The invention relates to the technical field of bridge pile detection, and provides a bridge pile foundation defect detection system based on an ultrasonic technology. The control terminal is used for controlling and managing the whole operation process of the system and monitoring data in real time; the multi-channel high-frequency ultrasonic detection module is used for transmitting ultrasonic signals through a multi-channel high-frequency probe array and receiving reflection signals in the bridge pile foundation to form original detection data; and the parallel computing and 3D imaging module is used for carrying out parallel computing processing on the received ultrasonic signal data and generating a three-dimensional structure image in the bridge pile foundation. The multi-channel high-frequency ultrasonic detection module is combined with the phased array technology and the multi-reflection wave processing technology, and high-precision detection of the internal structure of the bridge pile foundation is achieved. The phased array technology can flexibly control the propagation direction and depth of ultrasonic waves, and the coverage of a detection range and the resolution of signals are remarkably improved, so that the system can detect hidden defects such as micro cracks and holes.

The invention relates to the technical field of medical diagnosis systems, and discloses an AI physique diagnosis software analysis system which comprises an MCU module used for sending commands to execution modules and receiving and processing data signals of the execution modules to achieve diagnosis operation of the analysis system; the data acquisition module is used for collecting facial images and pulse information physiological indexes of the patient through an acquisition medium; the image processing module is used for analyzing facial features of the patient, the facial features comprise complexion, tongue coating and tongue quality, and corresponding evaluation data are generated through the obtained facial feature information. According to the AI physique diagnosis software analysis system, the accuracy and efficiency of traditional Chinese medicine physique identification are remarkably improved, the subjectivity and professional threshold are reduced, remarkable social and economic benefits are generated, and the system can more accurately evaluate the physique and display experimental data by using a deep learning algorithm and multi-modal data analysis.

The invention discloses a traditional Chinese medicine planting site selection evaluation method, system and device based on big data and a medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring a traditional Chinese medicine category of to-be-planted traditional Chinese medicine and soil data and meteorological data of a plurality of candidate planting areas; based on the plant growth parameters corresponding to the traditional Chinese medicine categories, establishing a planting area evaluation standard; performing evaluation processing on the soil data and the meteorological data of each candidate planting area according to a planting area evaluation standard to obtain an environmental adaptability index; receiving the multispectral image of each candidate planting area, determining the vegetation coverage and water and soil loss rate of each candidate planting area, and calculating an environmental risk index; and determining corresponding site selection priorities in combination with the environmental adaptability indexes and the environmental risk indexes, and taking the candidate planting area with the highest site selection priority as a target site selection area of the to-be-planted traditional Chinese medicine. By implementing the technical scheme provided by the invention, the accuracy of traditional Chinese medicine planting site selection evaluation can be improved.

The invention discloses a method for automatically screening session flood based on actually-measured daily average flow. The method specifically comprises the following steps that 1, a peak threshold value is set from actually-measured daily average flow data, and a flood maximum value is searched for to determine a flood peak value; 2, judging the independence of the peak values by setting time interval limitation between two adjacent peak values and flow minimum value limitation of a peak value interval; 3, determining the starting and ending time of the session flood by setting a session flood starting and ending flow threshold value and a time interval limit of a corresponding flood peak; and 4, when the starting and ending time ranges of the flood process corresponding to the two independent peak values are partially overlapped, the session flood is segmented. According to the method provided by the invention, the flood peak position can be accurately identified, and the screening efficiency is improved.

The invention relates to the technical field of network fault detection, in particular to an optical fiber network fault automatic positioning system and method, and the method comprises the steps: collecting optical signal transmission parameters of an optical fiber link; analyzing the transmission parameters of the received optical signal, and identifying an abnormal reflection signal; determining a fault type and a corresponding fault position by adopting a support vector machine according to the characteristics of the abnormal reflection signal; and matching a corresponding maintenance suggestion based on the fault type and the fault position. According to the automatic positioning method for the optical fiber network fault, efficient and accurate identification and positioning of the optical fiber network fault are realized by integrating a data acquisition technology, intelligent data analysis and a machine learning algorithm, and the efficiency and quality of network maintenance are greatly improved.

The invention provides a method for determining a mixing ratio, a storage medium and an electronic device, and the method comprises the steps: determining particle attribute parameters of a target material, and obtaining the particle attribute parameters of the target material under the condition of obtaining simulation data corresponding to mixing equipment; determining a first contact parameter of the target material and the mixing equipment based on an equipment intrinsic parameter and a particle attribute parameter in the simulation data, and determining a target mechanical basic model of the target material matched with a motion attribute under the condition that the motion attribute of the mixing equipment input by the target object is received, inputting the particle attribute parameter, the first contact parameter and the target mechanical basic model into a preset engineering application, and setting a particle generation strategy corresponding to the mixing equipment based on the mixing demand of the target object in the preset engineering application to obtain a to-be-operated simulation engineering application file, and operating the to-be-operated simulation engineering application file. And generating the target ratio parameter of each sub-strategy in the particle generation strategy. The problem that the coating is not uniform due to inappropriate ratio in the related technology is solved.

The invention belongs to the technical field of image segmentation, and discloses a multi-modal medical image automatic segmentation method and system based on a three-dimensional reconstruction model.The multi-modal medical image automatic segmentation method comprises the steps that multi-modal image data are acquired, and local features of the multi-modal image data are extracted; identifying a patient tissue structure in the multi-modal image data based on the local features; segmenting the tissue structure of the patient in the multi-modal image data by using a segmentation technology, and reconstructing a three-dimensional virtual model based on the tissue structure of the patient; and fusing a three-dimensional virtual model body drawing result and a three-dimensional virtual model surface drawing result to realize three-dimensional display of a segmentation result. According to the method, the influence of human errors and subjective judgment is reduced by identifying and segmenting the tissue structure, so that boundaries of various tissues are identified more accurately, automatic image segmentation can be completed in a short time, and subsequent diagnosis and analysis can be performed more quickly.

The invention discloses a safety and health degree evaluation system for a small reservoir dam. The safety and health degree evaluation system comprises an on-site safety inspection module, a safety monitoring data analysis module, a flood prevention capacity rechecking module, a seepage safety evaluation module, a structure safety evaluation module and a dam safety and health degree total rating module. According to the method, comprehensive safety assessment is carried out on the dam by adopting multi-source information, and the uncertainty and subjective influence in the dam service performance assessment and early warning process are reduced.

The invention discloses a mining plan making method and system, and relates to the technical field of automatic management, and the method comprises the steps: obtaining a mine image and environment data; extracting multi-dimensional features of the mine image, and fusing the multi-dimensional features to obtain fused features; inputting the fusion features into a recognition model to obtain a mineral recognition result; combining the environment data with the mineral identification result to form a mine environment-resource distribution map; and inputting the mine environment-resource distribution map into the mining model to obtain a mine mining plan. According to the mining plan based on machine vision and a large model, real-time and high-definition monitoring of the mine environment is achieved by integrating the high-precision camera shooting function and the advanced video processing technology, subjectivity and errors of manual judgment are reduced, the mining process is accelerated, optimal configuration and efficient utilization of resources are achieved, and the mining efficiency is improved. And higher production efficiency and economic benefits are brought to mine enterprises.

The invention discloses a driving behavior risk detection method, system and device and a storage medium, and relates to the field of driving safety. The method comprises the following steps: collecting vehicle internal data, external environment data, driver biological characteristic data, vehicle-mounted video data and vehicle-mounted audio data; extracting a first feature from the vehicle internal data, extracting a second feature from the external environment data, extracting a third feature from the driver biological feature data, extracting a fourth feature from the vehicle-mounted video data, and extracting a fifth feature from the vehicle-mounted audio data; fusing the first feature, the second feature, the third feature, the fourth feature and the fifth feature to obtain a feature vector, and inputting the feature vector into a preset machine learning model to obtain a risk detection score; and when the risk detection score exceeds a threshold value, displaying risk information on a vehicle-mounted display screen and giving a voice prompt. By implementing the technical scheme provided by the invention, the driving risk can be comprehensively evaluated by integrating multi-source data, the early warning is timely performed, and the driving safety is improved.

The invention discloses an intelligent compilation system and method of a building construction scheme, and the system comprises a data preprocessing module which is used for obtaining historical construction scheme text data of each building engineering project category; the knowledge base construction module is used for constructing a construction scheme knowledge base of each construction engineering project category by establishing a vector index of a construction scheme vector database of each construction engineering project category; the retrieval information extraction module is used for performing keyword extraction on the construction scheme retrieval statement of the construction engineering project input by the user, and mapping and recalling the construction scheme text data of a plurality of corresponding construction engineering projects; and the construction scheme compiling module is used for obtaining a construction scheme compiling basis of the construction engineering project. Associated storage and value utilization of previous engineering construction schemes are achieved, the scheme compiling efficiency and reliability are improved through intelligent generation of the scheme compiling basis, and effective guarantee is provided for the scheme compiling quality.

The invention belongs to the technical field of knowledge graph construction, and particularly relates to a multi-source heterogeneous data fusion and knowledge graph automatic construction system, which comprises a semantic extraction module for extracting minimum semantic fragments from multi-source data and performing cross-source alignment to generate a symbolized semantic framework; the data purification module converts multi-source data into symbolic predicates according to the framework, filters low-evidence data and outputs a purification set; the ternary generation module inputs the initial triad candidate set into a minimum rule grammar, and extracts entities, relationships and attributes to generate an initial triad candidate set; the disambiguation calibration module generates entity two-hop topological fingerprints based on the candidate set, completes disambiguation alignment and corrects conflicts, and outputs unambiguous structured knowledge; the mapping fusion module fuses the mapping information with the minimum connected ontology, and constructs and verifies an initial mapping knowledge domain; and the incremental updating module processes newly added data, performs incremental merging and maintains consistency, and forms a complete knowledge graph. According to the method, through full-link automatic construction, efficient fusion and high-quality atlas are realized.

The invention discloses a power transmission line foreign matter identification method and system based on an improved YOLOv10 model. Relates to the technical field of power transmission line detection. According to the method, the method is improved on the basis of the existing target detection technology in the scene of a special power transmission line, the structure of a YOLOv10 model is optimized and improved by introducing an ODConv dynamic convolution module, a CAFM hybrid convolution attention mechanism module and an NWD loss function on the basis of the YOLOv10 model, in the detection stage, foreign matters are recognized through a foreign matter detection model, and the foreign matters are recognized through a foreign matter recognition module. Inputting the image containing the foreign matter into a component detection model to determine the position of the component, and finally performing hazard assessment and making a response according to the relative position of the foreign matter and the component. The detection precision of the YOLOv10 model is effectively improved, the hazard assessment capability is improved, and the detection efficiency is improved, so that the efficiency of foreign matter removal work of the power transmission line is improved, data management can be performed, powerful support is provided for operation and maintenance of the power transmission line, and safe and stable operation of electric power is guaranteed.

The invention provides a method and system for interpreting expression of ER and PR in breast cancer by using artificial intelligence, and relates to the technical field of artificial intelligence, the method comprises the following steps: collecting labeled breast cancer tissue pathological section images, and performing data enhancement to obtain a sample image set; performing chromatic aberration balance on the image data in the sample image set to obtain a preprocessed data set; training a preset initial segmentation network by using the preprocessed data set; inputting the breast cancer tissue pathological section image into the trained cell nucleus segmentation model to obtain a cell nucleus region image; performing morphological processing on the cell nucleus region image to obtain a processed target region; and extracting color features and morphological features in the target area, and inputting the color features and the morphological features into the classification model to obtain an interpretation result. According to the method, cell nucleus segmentation and feature extraction are carried out through the deep learning model, subjectivity and errors of manual interpretation are reduced, and the interpretation accuracy is improved.

The invention discloses a static forest fire risk assessment method and system based on multi-source data and a remote sensing technology. The method comprises the following steps: establishing an index system of a target area and carrying out data acquisition; the collected data are preprocessed; calculating influence factors and index weights by using the preprocessed data, determining spatial and temporal distribution of calculation results of different influence factors, and calculating a comprehensive factor result by using each single influence factor according to a set weight; and evaluating the forest fire risk based on the obtained comprehensive factor result, and realizing dynamic monitoring of the forest fire risk value. According to the method, the accuracy and the real-time performance of forest fire space-time risk prediction are improved; and theoretical support is provided for follow-up forest fire risk prevention and control of electric power companies.

The invention discloses a design manuscript code generation and visual error iterative correction method and system based on multi-agent cooperation, and aims to solve the problems of low front-end code generation efficiency, poor visual restoration degree and lack of an automatic correction mechanism in the prior art. The system constructs a four-node state diagram based on LangGraph, automatically initializes a series engineering scaffold, obtains design data and generates a structured protocol, and generates a component code based on the protocol and performs visual verification and iterative correction on four processing nodes. The method comprises the following steps: dynamically generating an engineering scaffold through a large language model agent, converting Figma design data into a structured protocol, and carrying out iterative optimization by utilizing a judger-repairer mode until an error is lower than a threshold value; according to the method, high-fidelity automatic delivery from the design manuscript to the code is realized, and the executable front-end engineering code and the visual reduction degree analysis report are output.

The invention discloses a vacuum freeze dryer, particularly relates to the technical field of vacuum freeze drying, and comprises a drying chamber, a refrigerating system, a vacuum system, a heating system, a control system and an energy recovery device. The drying chamber is used for containing materials, the inner wall of the drying chamber is made of efficient heat insulation materials, the refrigerating system comprises a high-precision temperature sensor and an intelligent temperature controller, the refrigerating temperature fluctuation is accurately controlled within + / -1 DEG C, the vacuum degree fluctuation of the vacuum system is kept within + / -5Pa through a vacuum degree sensor and a stabilizing device, and the heating system adopts uniform heating elements. And it is ensured that the heating temperature nonuniformity is smaller than + / -1 DEG C, a control system achieves automatic drying, and an energy recovery device recovers waste cold and waste heat for other links. The problems that an existing vacuum freeze dryer is low in temperature control precision, unstable in vacuum degree, low in automation degree, low in energy utilization rate and the like are solved, the material quality can be effectively kept, the drying efficiency is improved, and energy consumption is reduced.

The invention relates to an X-ray-based defect identification method, belongs to the technical field of X-ray nondestructive testing, and solves the problems of low accuracy and efficiency of casting defect identification in the prior art. The method comprises the following steps: acquiring an X-ray image of a sample casting, and preprocessing the X-ray image to acquire an edge image of the sample casting; marking whether the contour has defects and defect types according to the area and defect features of each contour in the edge image of the sample casting; training a defect identification model by adopting the marked edge image of the sample casting; obtaining an X-ray image of a target casting, and preprocessing the X-ray image to obtain an edge image of the target casting; and inputting the edge image of the target casting into the trained defect identification model so as to identify whether the target casting has a defect and identify the type of the defect. According to the method, through combination of an X-ray imaging technology, an image processing technology and a machine learning technology, rapid and accurate identification of the internal defects of the casting is realized.

The embodiment of the invention belongs to the field of risk assessment, and relates to an enterprise risk assessment method, which comprises the steps of extracting key indexes from enterprise operation data of an enterprise; calculating a first weight of each key index based on an entropy method; calculating a correlation coefficient between the key indexes, and adjusting the first weight of each key index based on the obtained correlation coefficient to obtain a second weight of each key index; for each key index, based on a preset time attenuation algorithm and the second weight of the key index, decomposing the key index to each preset time interval to obtain a sub-index weight of the key index in each time interval; according to the sub-index weight of the key index in each time interval and the corresponding sub-index value, calculating the index score of the key index, and calculating the comprehensive score of the enterprise according to the index score of each key index; and generating an enterprise risk assessment result of the enterprise according to the comprehensive score. According to the invention, the accuracy and efficiency of enterprise risk assessment are improved.

The invention discloses a system for measuring the credibility of drivers with different temperament types for automatic driving. The system comprises a driver temperament type distinguishing module, a credibility measuring module, a credibility grade grading module, a credibility grade judging module and a comprehensive credibility evaluation module. According to the method, the difference of the credibility of drivers with different temperament types on automatic driving can be analyzed, and different coping strategies are adopted for the drivers with different temperament types; the credibility is measured by combining subjective factors and objective factors, and the measurement accuracy is improved.